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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/GenReality/blob/master/GEN_2_classify_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5z2GTD3zrIX"
      },
      "source": [
        "#@title INSTALLS\n",
        "!pip install livelossplot --quiet"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEP8pl8zPyiB"
      },
      "source": [
        "#@title IMPORTS\n",
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "import torchvision \n",
        "import torchvision.datasets as dset\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from livelossplot import PlotLosses"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
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        },
        "outputId": "fbc674f2-56a6-46f4-d5ba-dd7e7bea0de4"
      },
      "source": [
        "#@title DOWNLOADING DATA\n",
        "trans = transforms.Compose([transforms.ToTensor(), \n",
        "                            transforms.Normalize((0.5,), (1.0,))])\n",
        "# if not exist, download mnist dataset\n",
        "train_set = dset.MNIST(root='./data',train=True, \n",
        "                       transform=trans, download=True)\n",
        "test_set = dset.MNIST(root='./data',train=False, \n",
        "                      transform=trans, download=True)\n",
        "\n",
        "print(train_set)\n",
        "print(test_set)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55e08cfcaa3e4fdea4d1e26e2c6b01f9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n",
            "\n",
            "\n",
            "Dataset MNIST\n",
            "    Number of datapoints: 60000\n",
            "    Root location: ./data\n",
            "    Split: Train\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               ToTensor()\n",
            "               Normalize(mean=(0.5,), std=(1.0,))\n",
            "           )\n",
            "Dataset MNIST\n",
            "    Number of datapoints: 10000\n",
            "    Root location: ./data\n",
            "    Split: Test\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               ToTensor()\n",
            "               Normalize(mean=(0.5,), std=(1.0,))\n",
            "           )\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWB0sjGPQ9pL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1972ac6d-f8d0-4cb3-e9ef-4046e088c540"
      },
      "source": [
        "#@title SETUP DATALOADERS\n",
        "batch_size = 64\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "                 dataset=train_set,\n",
        "                 batch_size=batch_size,\n",
        "                 shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "                dataset=test_set,\n",
        "                batch_size=batch_size,\n",
        "                shuffle=False)\n",
        "\n",
        "print(len(train_loader))\n",
        "print(len(test_loader))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "938\n",
            "157\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mU9Y0M2knja2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "outputId": "d5037286-f3e8-4089-8822-02194675a0de"
      },
      "source": [
        "#@title PREVIEW THE DATA\n",
        "def imshow(img):\n",
        "  img = img / 2 + 0.5     \n",
        "  npimg = img.numpy()\n",
        "  plt.figure(figsize=(10, 10))\n",
        "  plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "  plt.show()\n",
        "\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "imshow(torchvision.utils.make_grid(images, nrow=8))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8EJIkih80S8"
      },
      "source": [
        "#@title MODEL\n",
        "class ConvNet(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(ConvNet, self).__init__()\n",
        "    self.conv1 = nn.Conv2d(1, 32, 3, 1)\n",
        "    self.conv2 = nn.Conv2d(32, 64, 3, 1)\n",
        "    self.dropout1 = nn.Dropout2d(0.25)\n",
        "    self.dropout2 = nn.Dropout2d(0.5)\n",
        "    self.fc1 = nn.Linear(9216, 128)\n",
        "    self.fc2 = nn.Linear(128, 10)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.conv1(x)\n",
        "    x = F.relu(x)\n",
        "    x = self.conv2(x)\n",
        "    x = F.relu(x)\n",
        "    x = F.max_pool2d(x, 2)\n",
        "    x = self.dropout1(x)\n",
        "    x = torch.flatten(x, 1)\n",
        "    x = self.fc1(x)\n",
        "    x = F.relu(x)\n",
        "    x = self.dropout2(x)\n",
        "    x = self.fc2(x)\n",
        "    output = F.log_softmax(x, dim=1)\n",
        "    return output"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xSn_LBQRhNj"
      },
      "source": [
        "#@title MODEL, OPTIMIZER and LOSS FUNCTION\n",
        "model = ConvNet()\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "loss_fn = nn.CrossEntropyLoss()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWxfkhedRu3W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "97c9c904-3dc5-475d-d12f-24d1be5d0e80"
      },
      "source": [
        "#@title TRAINING\n",
        "liveloss = PlotLosses()\n",
        "epochs = 10\n",
        "history={}\n",
        "for epoch in range(epochs):  \n",
        "  avg_loss = 0\n",
        "  for batch_idx, (x, y) in enumerate(train_loader):\n",
        "    optimizer.zero_grad()    \n",
        "    x, y = Variable(x), Variable(y)\n",
        "    y_pred = model(x)\n",
        "    loss = loss_fn(y_pred, y)\n",
        "    avg_loss = avg_loss * 0.9 + loss.data * 0.1\n",
        "    history['avg_loss'] = avg_loss\n",
        "    loss.backward()\n",
        "    optimizer.step()   \n",
        "    liveloss.update(history)\n",
        "    liveloss.send() \n",
        "    "
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-e80062ff8138>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mliveloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mliveloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/livelossplot/plot_losses.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;34m\"\"\"Method will send logs to every output class\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/livelossplot/outputs/matplotlib_plot.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, logger)\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mextra_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_after_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_default_after_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_label\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/livelossplot/outputs/matplotlib_plot.py\u001b[0m in \u001b[0;36m_default_after_plots\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_idx\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_draw_metric_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_logs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mLogItem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_label\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    270\u001b[0m     \"\"\"\n\u001b[1;32m    271\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     37\u001b[0m             display(\n\u001b[1;32m     38\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m             )\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(*objs, **kwargs)\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    307\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2098\u001b[0m                            else suppress())\n\u001b[1;32m   2099\u001b[0m                     \u001b[0;32mwith\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2100\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2101\u001b[0m                     \u001b[0mbbox_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bbox_extra_artists\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2102\u001b[0m                     bbox_inches = self.figure.get_tightbbox(renderer,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1736\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2628\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2629\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2630\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2631\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2632\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1225\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_gid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1227\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1228\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1229\u001b[0m                                                                 renderer)\n",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mround_\u001b[0;34m(a, decimals, out)\u001b[0m\n\u001b[1;32m   3597\u001b[0m     \u001b[0maround\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mequivalent\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0msee\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdetails\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3598\u001b[0m     \"\"\"\n\u001b[0;32m-> 3599\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0maround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36maround\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36maround\u001b[0;34m(a, decimals, out)\u001b[0m\n\u001b[1;32m   3222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3223\u001b[0m     \"\"\"\n\u001b[0;32m-> 3224\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'round'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}